Refined reconstruction of time-varying data

ABSTRACT

Systems and methods are provided for refined data reconstruction. In accordance with one aspect, the framework performs a first four-dimensional reconstruction of time-varying data to generate a four-dimensional Digital Subtraction Angiography (DSA) dataset of an object of interest. The framework extracts a volume of interest from the four-dimensional DSA dataset to generate a volume array. The volume of interest may be refined based on the volume array to generate a refined dataset. A second four-dimensional reconstruction may then be performed based on the refined dataset to generate a zoomed-in four-dimensional representation of the volume of interest.

TECHNICAL FIELD

The present disclosure generally relates to image data processing, andmore particularly to refined reconstruction of time-varying data.

BACKGROUND

Angiography is a common method used to represent blood vessels based ondiagnostic imaging methods, such as X-ray or Magnetic ResonanceTomography (MRT). For an improved representation of the vessels underexamination, Digital Subtraction Angiography (DSA) has been developed.DSA is a fluoroscopy technique used in interventional radiology toclearly visualize blood vessels in a bony or dense soft tissueenvironment. Images are produced by subtracting a ‘pre-contrast image’or the mask from subsequent images acquired after the contrast mediumhas been introduced into a structure or tissue of interest. These imagescan be used to provide time-resolved or time-varying information thatshows the development of the structure or tissue of interest over time.

In current clinical practice, time-resolved information is generallyonly available in two dimensions. Typically, the surgeon has to performa two-dimensional (2D) to three-dimensional (3D) mental conversion fromthe 2D projection images to 3D anatomy in order to assess and diagnosevascular pathologies and blood flow abnormalities. The filling of thevasculature changes from frame to frame, leaving the surgeon with thedifficult task of interpreting 3D filling from varying 2D snapshots.Regardless of acquisition/viewing angle, vessel segments that areoverlapping and/or obscured may therefore be compromised, leading topotentially missing image information or incorrect diagnosis. Problemsinclude, for example, vessel overlap or vessels running orthogonal tothe detector plane.

Vascular filling may be visualized using single-plane or bi-plane 2D DSAimage(s). Existing methods may provide acceptable results, but strugglewith complex vasculature and occluded vessels, introduction of prematurevessel filling, and fluctuations in vessel filling. Some traditionaltechniques use data from static angles instead of the acquisitionsequences themselves, which can lead to additional radiation exposurefor the patient as well as inaccuracies arising from a correspondingneed for highly accurate image registration steps. Other methods arebased on simplifying assumptions (i.e., simplified models) concerningthe patient's physiology (e.g., periodic cardiac activity) as well asthe transport of blood, and mixture of blood and contrast agent, throughthe patient's vasculature. These may lead to reconstructed flow resultsthat deviate from real flow patterns.

Recent years have seen the introduction of methodologies fornon-time-resolved 3D-DSA. In one method, a mask projection imagesequence is first acquired during a rotational scan of the angiographicdevice, followed by a sequence of rotational fill projection imagesacquired after the introduction of contrast agent. The mask projectionimages are subtracted from the fill projection images to generateprojection image data that displays a subject's vascular anatomyacquired at different viewing angles. Using 3D reconstructiontechniques, a static volumetric dataset of a subject's vasculature canbe created. This static reconstruction does not, however, include thechange recorded in the acquisition temporal sequence.

SUMMARY

Described herein are systems and methods for refined datareconstruction. In accordance with one aspect, the framework performs afirst four-dimensional reconstruction of time-varying data to generate afour-dimensional Digital Subtraction Angiography (DSA) dataset of anobject of interest. The framework extracts a volume of interest from thefour-dimensional DSA dataset to generate a volume array. The volume ofinterest may be refined based on the volume array to generate a refineddataset. A second four-dimensional reconstruction may then be performedbased on the refined dataset to generate a zoomed-in four-dimensionalrepresentation of the volume of interest.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the followingdetailed description. It is not intended to identify features oressential features of the claimed subject matter, nor is it intendedthat it be used to limit the scope of the claimed subject matter.Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings.

FIG. 1 is a block diagram illustrating an exemplary system;

FIG. 2 shows an exemplary method of data reconstruction performed by acomputer system;

FIG. 3 shows a time series of 3D angiographic images of a patient'sbrain that were reconstructed using a single rotating plane;

FIG. 4 illustrates an initial estimate of an exemplary 4D-DSAreconstruction;

FIG. 5 shows a final estimate of the exemplary 4D-DSA reconstruction;

FIG. 6 illustrates an initial estimate of an exemplary refined 4D-DSAreconstruction of the center vessel; and

FIG. 7 illustrates a final estimate of the exemplary refined 4D-DSAreconstruction of a center vessel.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, devices, methods, etc., inorder to provide a thorough understanding of implementations of thepresent framework. It will be apparent, however, to one skilled in theart that these specific details need not be employed to practiceimplementations of the present framework. In other instances, well-knownmaterials or methods have not been described in detail in order to avoidunnecessarily obscuring implementations of the present framework. Whilethe present framework is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that there is no intent to limit theconcept to the particular forms disclosed, but on the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the concept. Furthermore, forease of understanding, certain method steps are delineated as separatesteps; however, these separately delineated steps should not beconstrued as necessarily order dependent in their performance.

The term “X-ray image” as used herein may mean a visible X-ray image(e.g., displayed on a video screen) or a digital representation of anX-ray image (e.g., a file corresponding to the pixel output of an X-raydetector). The term “in-treatment X-ray image” as used herein may referto images captured at any point in time during a treatment deliveryphase of an interventional or therapeutic procedure, which may includetimes when the radiation source is either on or off. From time to time,for convenience of description, CT imaging data (e.g., cone-beam CTimaging data) may be used herein as an exemplary imaging modality. Itwill be appreciated, however, that data from any type of imagingmodality including but not limited to X-ray radiographs, MRI, PET(positron emission tomography), PET-CT, SPECT, SPECT-CT, MR-PET, 3Dultrasound images or the like may also be used in variousimplementations.

Unless stated otherwise as apparent from the following discussion, itwill be appreciated that terms such as “segmenting,” “generating,”“registering,” “determining,” “aligning,” “positioning,” “processing,”“computing,” “selecting,” “estimating,” “detecting,” “tracking” or thelike may refer to the actions and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices. Embodiments of the methods described herein may be implementedusing computer software. If written in a programming language conformingto a recognized standard, sequences of instructions designed toimplement the methods can be compiled for execution on a variety ofhardware platforms and for interface to a variety of operating systems.In addition, implementations of the present framework are not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages may be used.

As used herein, the term “image” refers to multi-dimensional datacomposed of discrete image elements (e.g., pixels for 2D images, voxelsfor 3D images, doxels for 4D datasets). The image may be, for example, amedical image of a subject collected by computer tomography, magneticresonance imaging, ultrasound, or any other medical imaging system knownto one of skill in the art. The image may also be provided fromnon-medical contexts, such as, for example, remote sensing systems,electron microscopy, etc. Although an image can be thought of as afunction from R³ to R, or a mapping to R³, the present methods are notlimited to such images, and can be applied to images of any dimension,e.g., a 2D picture, 3D volume or 4D dataset. For a 2- or 3-Dimensionalimage, the domain of the image is typically a 2- or 3-Dimensionalrectangular array, wherein each pixel or voxel can be addressed withreference to a set of 2 or 3 mutually orthogonal axes. The terms“digital” and “digitized” as used herein will refer to images orvolumes, as appropriate, in a digital or digitized format acquired via adigital acquisition system or via conversion from an analog image.

The terms “pixels” for picture elements, conventionally used withrespect to 2D imaging and image display, “voxels” for volume imageelements, often used with respect to 3D imaging, and “doxels” for 4Ddatasets can be used interchangeably. It should be noted that the 3Dvolume image is itself synthesized from image data obtained as pixels ona 2D sensor array and displays as a 2D image from some angle of view.Thus, 2D image processing and image analysis techniques can be appliedto the 3D volume image data. In the description that follows, techniquesdescribed as operating upon doxels may alternately be described asoperating upon the 3D voxel data that is stored and represented in theform of 2D pixel data for display. In the same way, techniques thatoperate upon voxel data can also be described as operating upon pixels.In the following description, the variable x is used to indicate asubject image element at a particular spatial location or, alternatelyconsidered, a subject pixel. The terms “subject pixel”, “subject voxel”and “subject doxel” are used to indicate a particular image element asit is operated upon using techniques described herein.

One aspect of the present framework facilitates interrogation of afour-dimensional (4D) Digital Subtraction Angiography (DSA) dataset. A4D dataset generally refers to a time-resolved three-dimensional (3D)dataset. The 4D DSA dataset may be derived from a pair of rotationalacquisitions: a rotational mask run that generates mask projection imagedata followed by a rotational contrast-enhanced fill run that generatesfill projection image data. The temporal dynamics contained in theprojection image data may be functionally encoded into static 3D-DSAconstraining image data to generate the 4D-DSA dataset.

Current techniques only reconstruct 4D-DSA datasets based on the fullyacquired field of view (FOV). The present framework advantageouslyenables the user to refine a reconstruction of a 4D-DSA dataset by, forexample, zooming in and/or cropping a volume of interest. A dedicatedre-reconstruction of the 4D-DSA dataset may then be performed on thevolume of interest. The volume of interest may be refined by, forexample, increasing the spatial resolution relative to the regionsoutside the volume of interest or altering the vascular informationoutside the volume of interest. The accuracy of the reconstructed seriesof time-resolved volumes for a volume of interest can therefore besignificantly improved, enabling more detailed qualitative andquantitative analysis of the underlying hemodynamics in thereconstruction.

The refined 4D-DSA datasets reconstructed by the present framework maybe used to, for instance, evaluate risks prior to neurovascularinterventions. Neurovascular interventions commonly involve theplacement of flow diverting devices to cover aneurysms, and to inducethrombolysis and subsequent healing of the aneurysm. These flowdiverting stents can substantially alter the flow dynamics in thevasculature upstream from the aneurysm, creating potentially dangerousscenarios. Misplaced stents ending in a vessel wall or with a highlyreduced diameter (nozzle effect) can increase the risk of a new aneurysmcreation or vessel dissection. Evaluating these risks prior tointervention can advantageously mitigate the risks of such procedures.It is understood that while a particular application directed tovascular network visualization may be shown, the technology is notlimited to the specific implementations illustrated.

FIG. 1 is a block diagram illustrating an exemplary system 100. Thesystem 100 includes a computer system 101 for implementing the frameworkas described herein. Computer system 101 may be a desktop personalcomputer, a portable laptop computer, another portable device, amini-computer, a mainframe computer, a server, a cloud infrastructure, astorage system, a dedicated digital appliance, a communication device,or another device having a storage sub-system configured to store acollection of digital data items. In some implementations, computersystem 101 operates as a standalone device. In other implementations,computer system 101 may be connected (e.g., using a network) to othermachines, such as imaging device 102 and workstation 103. In a networkeddeployment, computer system 101 may operate in the capacity of a server(e.g., thin-client server, such as syngo®.via by Siemens Healthcare), aclient user machine in server-client user network environment, or as apeer machine in a peer-to-peer (or distributed) network environment.

Computer system 101 may include a processor device or central processingunit (CPU) 104 coupled to one or more non-transitory computer-readablemedia 105 (e.g., computer storage or memory), display device 108 (e.g.,monitor) and various input devices 110 (e.g., mouse or keyboard) via aninput-output interface 121. Computer system 101 may further includesupport circuits such as a cache, a power supply, clock circuits and acommunications bus. Various other peripheral devices, such as additionaldata storage devices and printing devices, may also be connected to thecomputer system 101.

The present technology may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof, either as part of the microinstruction code or as part of anapplication program or software product, or a combination thereof, whichis executed via the operating system. In one implementation, thetechniques described herein are implemented as computer-readable programcode tangibly embodied in one or more non-transitory computer-readablemedia 105. In particular, the present techniques may be implemented by areconstruction unit 106 and a refinement unit 107. Non-transitorycomputer-readable media 105 may include random access memory (RAM),read-only memory (ROM), magnetic floppy disk, flash memory, and othertypes of memories, or a combination thereof. The computer-readableprogram code is executed by processor device 104 to process images orimage data acquired by, for example, imaging device 102. As such, thecomputer system 101 is a general-purpose computer system that becomes aspecific purpose computer system when executing the computer-readableprogram code. The computer-readable program code is not intended to belimited to any particular programming language and implementationthereof. It will be appreciated that a variety of programming languagesand coding thereof may be used to implement the teachings of thedisclosure contained herein.

The same or different computer-readable media 105 may be used forstoring image datasets, dynamic reconstruction instructions, knowledgebase, and so forth. Such data may also be stored in external storage orother memories. The external storage may be implemented using a databasemanagement system (DBMS) managed by the processor device 104 andresiding on a memory, such as a hard disk, RAM, or removable media. Theexternal storage may be implemented on one or more additional computersystems. For example, the external storage may include a data warehousesystem residing on a separate computer system, a picture archiving andcommunication system (PACS), or any other now known or later developedhospital, medical institution, medical office, testing facility,pharmacy or other medical patient record storage system.

The imaging device 102 may be a radiology scanner, such as an X-ray or aCT scanner, for acquiring image data. The workstation 103 may include acomputer and appropriate peripherals, such as a keyboard and displaydevice, and can be operated in conjunction with the entire system 100.For example, the workstation 103 may communicate with the imaging device102 so that the image data collected by the imaging device 102 can berendered at the workstation 103 and viewed on a display device.

The workstation 103 may communicate directly with the computer system101 to display processed image data and/or output image processingresults (e.g., 4D DSA dataset). The workstation 103 may include agraphical user interface to receive user input via an input device(e.g., keyboard, mouse, touch screen, voice or video recognitioninterface, etc.) to manipulate visualization and/or processing of theimage data. For example, the user may view the processed image data, andspecify one or more view adjustments or preferences (e.g., zooming,cropping, panning, rotating, changing contrast, changing color, changingview angle, changing view depth, changing rendering or reconstructiontechnique, etc.), navigate to a particular region of interest byspecifying a “goto” location, navigate (e.g., stop, play, step through,etc.) temporal volumes of the reconstructed 4D dataset, and so forth.

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystems components (or the process steps) may differ depending upon themanner in which the present framework is programmed. Given the teachingsprovided herein, one of ordinary skill in the related art will be ableto contemplate these and similar implementations or configurations ofthe present framework.

FIG. 2 shows an exemplary method 200 of data reconstruction performed bya computer system. It should be understood that the steps of the method200 may be performed in the order shown or a different order.Additional, different, or fewer steps may also be provided. Further, themethod 200 may be implemented with the system 101 of FIG. 1, a differentsystem, or a combination thereof.

At 202, reconstruction unit 106 performs a first 4D reconstruction oftime-varying image data to generate a 4D (or time-varying 3D) DSAprojection image dataset V(t) of an object of interest. Each doxel ofthe 4D DSA projection image dataset V(t) represents the injectedcontrast flow in the vasculature of the object of interest at aparticular three-dimensional location and at a particular time. Theobject of interest may be any biological object identified forinvestigation or examination, such as a portion of a patient's orsubject's brain, heart, leg, arm, and so forth. The object of interestincludes one or more vessel-like structures (e.g., blood vessels,arteries, vascular tree or network, etc.). The one or more vessel-likestructures may be dynamic or time-varying structures that can be filledwith a contrast agent or medium for observing its propagation over time.In some implementations, a static (i.e., non-temporal) 3D image data ofa device (e.g., flow diverting device) implanted in the object ofinterest is also reconstructed.

The time-varying data may be a set of 2D DSA projection images that areacquired by performing a rotational scan or angular acquisitions usingimaging device 102. A single mask and fill acquisition may be performedvia the imaging device 102. More particularly, a mask image dataset mayfirst be acquired via the imaging device 102 such that it can besubtracted from the corresponding time-varying contrast filledprojection image dataset. A mask image is simply an image of the samearea before the contrast agent (or medium) is administered to fill thevessel-like structures of the irradiated object of interest that is tobe investigated. The actual angular- and time-varying 2D projection datamay be based on a contrast enhanced acquisition that is initiated beforeor after the injection of X-ray contrast medium into the vessel-likestructures as the first inflow of contrast becomes visible. Both maskand fill runs may follow the same acquisition trajectory. The trajectorymay cover the entire field-of-view (FOV) range of a 3D DSA.

Imaging device 102 may be a scanner or C-arm system with a singleimaging plane or multiple imaging planes. For example, imaging device102 may be a flat-panel based X-ray scanner that includes at least onepair of X-ray source and X-ray detector. Alternatively, imaging device102 may include a rotating CT gantry covering at least one pair of X-raysource and X-ray detector. In other implementations, imaging device 102is an MR projection scanner. In yet other implementations, imagingdevice 102 is a rotating optical CT gantry covering at least one pair oflight source and optical detector. Other types of imaging device 102,such as angular sampling ultrasound, may also be used.

FIG. 3 shows a time series of 3D angiographic images 302 of a patient'sbrain that were reconstructed using a single rotating imaging plane.Methods for performing a 4D-DSA reconstruction of time-varying imagedata acquired by a single rotating plane C-arm system are described inapplication Ser. No. 14/302,596 filed on Jun. 12, 2014 (now U.S. Pub.No. 2014/0376791), which is hereby incorporated by reference. Thesemethods may determine time-varying volumetric attenuation curves of thevessel-like structures of interest, resulting in a 3D plus time (or4D-DSA) volumetric dataset that includes the time dimension. The 4D-DSAdataset may also be derived from time- and projection angle-varyingdata. Confidence values or curves may be used in performinginterpolation of time-resolved 3D DSA. Such framework may be appliedonce, or in an iterative fashion. The 4D-DSA dataset may also bedynamically and iteratively reconstructed based on, for example, aninitial time-varying 3D projection dataset derived from time-varying 2Dprojection data acquired at multiple angles.

Methods for performing a 4D-DSA reconstruction of time-varying imagedata acquired by a dual C-arm system are described in German applicationno. 102015224176.9 filed on Dec. 3, 2015 entitled “Tomography system andmethod for generating a sequence of volume images of a vasculature”(also PCT application no. PCT/EP2015/079102 filed on Dec. 9, 2015),which are hereby incorporated by reference. These techniques are basedon an angiographic biplane system that comprises two simultaneouslyrotating planes. The accuracy of the reconstructed series oftime-resolved volumes can be significantly improved, since informationfrom the two planes can be exploited to mitigate accuracy issues due tovascular overlap.

FIG. 4 illustrates an initial estimate of an exemplary 4D-DSAreconstruction in a simulation study. The initial estimate includes allvessels 402. Image 403 shows a current estimate of the 4D-DSAreconstruction. Image 404 shows the difference between the known groundtruth and the current estimate of the 4D-DSA reconstruction. Image 406shows a reconstruction of the error between the projection of thecurrent estimate and the original projection information. Image 408shows a sinogram of the error/difference in projection images. Timecurves 410 a-b are taken from the center vessel, wherein curve 410 arepresents the ground truth and curve 410 b represents the currentestimate. It can be observed that the error between the ground truth andthe estimate is very high.

FIG. 5 shows a final estimate of the exemplary 4D-DSA reconstruction inthe simulation study. Image 503 shows the current estimate of the 4D-DSAreconstruction, which includes all vessels 502. Image 504 shows thedifference between the known ground truth and the current estimate ofthe 4D-DSA reconstruction. Image 506 shows the reconstruction of theerror between the projection of the current estimate and the originalprojection information. Image 508 shows the sinogram of theerror/difference in projection images. Time curves 510 a-b are takenfrom the center vessel, wherein curve 510 a represents the ground truthand curve 510 b represents the current estimate. It can be observed thatthe error between the ground truth and the estimate of the 4D-DSAreconstruction has been minimized.

Returning to FIG. 2, at 204, refinement unit 107 identifies a volume ofinterest (VOI) in the resulting 4D DSA dataset generated byreconstruction unit 106. The volume of interest is any sub-region of anyshape (e.g., cubic) of the entire 4D DSA dataset that is identified forfurther study. The VOI may be manually, automatically orsemi-automatically identified. For example, the VOI may be identified byreceiving a user selection from a user interface implemented onworkstation 103, including coordinates of points defining the volume ofinterest. Reconstruction unit 106 may initiate a volumetric rendering ofthe 4D DSA dataset that is presented to the user via, for example, auser interface implemented on workstation 103. The time component may bedisplayed as, for example, a color rendering or time-steps generatedduring interaction. In some implementations, a rendering of the static3D image data of the implanted device is also presented with the 4D DSAdata via the user interface.

The user interface may provide various user interface elements (e.g.,buttons, text functions) to enable the user to select, zoom in and/orcrop the volume of interest in the 4D DSA dataset. The user interfacemay further provide a user interface element to enable the user toinitiate refinement of the volume of interest. Upon initiation of therefinement process, the workstation 103 may transmit the coordinates ofthe volume of interest to the refinement unit 107.

At 206, refinement unit 107 extracts the volume of interest (VOI) fromthe 4D angiographic image data V(t) to generate a volume array V(t)_(c).The extraction may be performed by, for example, subtracting the VOIfrom V(t).

At 208, refinement unit 107 refines the volume of interest (VOI) basedon the volume array V(t)_(c) to generate a new refined dataset. In someimplementations, the VOI is refined by removing vascular data in theregions outside the volume of interest. This may be achieved by forwardprojecting (A[V(t)_(c)]=p(t)_(c)) the volume array V(t)_(c) into thecorresponding projection image (or imaging plane) p(t) to generate anintermediate projection dataset p(t)_(c), and subtracting theintermediate projection dataset p(t)_(c) from the projection image p(t)to generate a new refined projection dataset p(t)_(VOI). The new refinedprojection dataset p(t)_(VOI) is a 2D image array containing onlyvascular data in the VOI.

Alternatively, the VOI is refined by changing the spatial resolutionsettings within the volume array V(t)_(c) and/or outside the volumearray V(t)_(c) such that the volume array V(t)_(c) has a higher spatialresolution relative to the regions outside the volume array V(t)_(c). Insome implementations, a new refined dataset V(t)_(spatial) is created,having a lower resolution outside the volume of interest (relativelylower than the initial reconstruction) and a very high spatialresolution inside the volume of interest (relatively higher than theinitial reconstruction). The new refined dataset V(t)_(spatial) may becreated by reconstructing the volume array V(t)_(c) from the refinedprojection dataset p(t)_(VOI) at a relatively smaller voxel size andreconstructing another volume array at a relatively larger voxel sizeusing conventional 4D DSA. The two volume arrays may then be merged,resulting in a refined dataset V(t)_(spatial) with a higher spatialresolution in the VOI and a lower spatial resolution outside the VOI.

In other implementations, the VOI is refined based on the principles offiltered backprojection (FBP). Recent research has proven that a dulysampled 3D dataset reconstructed with FBP contains all temporal dynamicsin the images. See, for example, Tang, Jie, et al., “New consistencytheorem of motion contaminated projection data and applications inmotion artifacts correction,” SPIE Medical Imaging. InternationalSociety for Optics and Photonics, 2012, which is hereby incorporated byreference. To illustrate, when forward projecting through a 3D volumeinto the acquired projection angles, the same image will be createdgiving the same temporal dynamics. This can be visualized as follows: arotational x-ray sequence of a glass being filled from empty to full isacquired and reconstructed using FBP. The averaging effect of FBP willpresent a half-full glass. If forward projections are generated throughthe 3D volume, they will represent the correct fill status of the glassat the time of the projection image acquisition.

The VOI may be refined by removing the defined volume array V(t)_(c)from a static 3D volume S(t) reconstructed by filtered backprojection(FBP) to generate a cropped static volume array S(t)_(c). The croppedstatic volume array S(t)_(c) may then be forward projected(A[S(t)_(c)]=s(t)_(c)) into the corresponding projection image (p(t)) togenerate an intermediate projection dataset s(t)_(c). A new refineddataset s(t)_(VOI) is created by subtracting the intermediate projectiondataset s(t)_(c) from projection image p(t). This new refined datasets(t)_(VOI) is a 2D array containing only the vascular data in the VOI.

FIG. 6 illustrates an initial estimate of an exemplary refined 4D-DSAreconstruction of the center vessel 602. The initial estimate of the4D-DSA reconstruction includes only the volume of interest. Image 603shows the current estimate of the 4D-DSA vessel of interestreconstruction. Image 604 shows the difference between the known groundtruth and the current estimate of the 4D-DSA reconstruction. Image 606shows the reconstruction of the error between the projection of thecurrent estimate and the original projection information. Image 608shows a sinogram of the error/difference in projection images. Timecurves 610 a-b are taken from the center vessel, wherein curve 610 arepresents the ground truth while curve 610 b represents the currentestimate. It can be observed that the error between the ground truth andestimate is very high.

In FIG. 2, at 210, refinement unit 107 invokes reconstruction unit 106to perform a second 4D-DSA reconstruction based on the refined datasetto generate a zoomed-in 4D representation of the VOI. In someimplementations, the second 4D-DSA reconstruction is performed withinone or more boundaries of the refined projection dataset p(t)_(VOI),effectively creating a zoomed-in 4D-DSA volume array V(t)_(VOI).

Alternatively, the second 4D-DSA reconstruction is performed using thenew refined dataset V(t)_(spatial), resulting in a 4D representation ofthe VOI with a relatively increased spatial resolution of thevasculature in the VOI. The 4D representation can then be displayed withor without the vasculature outside the VOI.

In other implementations, the second 4D-DSA reconstruction is within oneor more boundaries of the refined dataset s(t)_(VOI), effectivelycreating a zoomed-in 4D-DSA volume array V(t)_(VOI). With the limitedamount of data to be reconstructed, an iterative reconstruction schemecan be used, thereby achieving superior accuracy performance whencompared to conventional filtered backprojection reconstruction.

This 4D-DSA reconstruction step can be performed using any of theaforementioned reconstruction methods, such as those described inapplication Ser. No. 14/302,596 filed on Jun. 12, 2014 and Germanapplication no. 102015224176.9 filed on Dec. 3, 2015. With the limitedamount of data to be reconstructed, an iterative reconstruction schemecan be used to achieve superior accuracy performance when compared toconventional filtered backprojection reconstruction. New 2D projectiondatasets may be reconstructed using the established methods.

FIG. 7 illustrates a final estimate of the exemplary refined 4D-DSAreconstruction of the center vessel 702. The final estimate of the4D-DSA reconstruction includes only the volume of interest. Image 703shows the current 4D-DSA reconstruction of the center vessel 702. Image704 shows the difference between the known ground truth and the current4D-DSA reconstruction. Image 706 illustrates the reconstruction of theerror between the projection of the current estimate and the originalprojection information. Image 708 depicts a sinogram of theerror/difference in projection images. Time curves 710 a-b are takenfrom the center vessel, wherein curve 710 a represents the ground truthand curve 710 b represents the estimate. It can be observed that theerror between the ground truth and the final estimate is minimized.

While the present framework has been described in detail with referenceto exemplary embodiments, those skilled in the art will appreciate thatvarious modifications and substitutions can be made thereto withoutdeparting from the spirit and scope of the invention as set forth in theappended claims. For example, elements and/or features of differentexemplary embodiments may be combined with each other and/or substitutedfor each other within the scope of this disclosure and appended claims.

What is claimed is:
 1. A non-transitory computer-readable mediumembodying instructions executable by machine to perform operations fordata reconstruction comprising: performing a first four-dimensionalreconstruction of time-varying data to generate a four-dimensionalDigital Subtraction Angiography (DSA) dataset of an object of interest;identifying a volume of interest in the four-dimensional DSA dataset;extracting the volume of interest from the four-dimensional DSA datasetto generate a volume array; refining the volume of interest based on thevolume array to generate a refined dataset; and performing a secondfour-dimensional reconstruction based on the refined dataset to generatea zoomed-in four-dimensional representation of the volume of interest.2. The non-transitory computer-readable medium of claim 1 includingfurther instructions executable by machine to refine the volume ofinterest by removing vascular data outside the volume of interest.
 3. Asystem for data reconstruction, comprising: a non-transitory memorydevice for storing computer readable program code; and a processor incommunication with the memory device, the processor being operative withthe computer readable program code to: perform a first four-dimensionalreconstruction of time-varying data to generate a four-dimensionalDigital Subtraction Angiography (DSA) dataset of an object of interest,identify a volume of interest in the four-dimensional DSA dataset,extract the volume of interest from the four-dimensional DSA dataset togenerate a volume array, refine the volume of interest based on thevolume array to generate a refined dataset, and perform a secondfour-dimensional reconstruction based on the refined dataset to generatea zoomed-in four-dimensional representation of the volume of interest.4. The system of claim 3 further comprises an imaging device with asingle rotating imaging plane that acquires the time-varying data. 5.The system of claim 3 further comprises an imaging device with multiplerotating imaging planes that acquire the time-varying data.
 6. Thesystem of claim 3 wherein the processor is operative with the computerreadable program code to reconstruct a static three-dimensional imagedata of a device implanted in the object of interest.
 7. The system ofclaim 3 wherein the processor is operative with the computer readableprogram code to identify the volume of interest by receiving a userselection of the volume interest via a user interface.
 8. The system ofclaim 3 wherein the processor is operative with the computer readableprogram code to initiate a volumetric rendering of the four-dimensionalDSA dataset at the user interface.
 9. The system of claim 3 wherein theprocessor is operative with the computer readable program code to refinethe volume of interest by removing vascular data outside the volume ofinterest.
 10. The system of claim 9 wherein the processor is operativewith the computer readable program code to remove the vascular dataoutside the volume of interest by forward projecting the volume arrayinto a corresponding projection image to generate an intermediateprojection dataset, and subtracting the intermediate projection datasetfrom the projection image to generate the refined dataset.
 11. Thesystem of claim 3 wherein the processor is operative with the computerreadable program code to refine the volume of interest by changingspatial resolution settings within the volume array, outside the volumearray or a combination thereof to generate the refined dataset.
 12. Thesystem of claim 3 wherein the processor is operative with the computerreadable program code to refine the volume of interest based on filteredbackprojection.
 13. The system of claim 12 wherein the processor isoperative with the computer readable program code to refine the volumeof interest based on filtered backprojection by removing the volumearray from a static three-dimensional volume reconstructed by filteredbackprojection to generate a cropped static volume array, forwardprojecting the cropped static volume array into a correspondingprojection image to generate an intermediate projection dataset, andsubtracting the intermediate projection dataset from the projectionimage to generate the refined dataset.
 14. The system of claim 3 whereinthe processor is operative with the computer readable program code toperform the second four-dimensional reconstruction within one or moreboundaries of the refined dataset.
 15. A method of data reconstruction,comprising: performing a first four-dimensional reconstruction oftime-varying data to generate a four-dimensional Digital SubtractionAngiography (DSA) dataset of an object of interest; identifying a volumeof interest in the four-dimensional DSA dataset; extracting the volumeof interest from the four-dimensional DSA dataset to generate a volumearray; refining the volume of interest based on the volume array togenerate a refined dataset; and performing a second four-dimensionalreconstruction based on the refined dataset to generate a zoomed-infour-dimensional representation of the volume of interest.
 16. Themethod of claim 15 wherein refining the volume of interest comprisesremoving vascular data outside the volume of interest.
 17. The method ofclaim 15 wherein refining the volume of interest comprises changingspatial resolution settings within the volume array, outside the volumearray or a combination thereof to generate the refined dataset.
 18. Themethod of claim 15 wherein refining the volume of interest comprisesrefining the volume of interest based on filtered backprojection. 19.The method of claim 18 wherein refining the volume of interest based onfiltered backprojection comprises removing the volume array from astatic three-dimensional volume reconstructed by filtered backprojectionto generate a cropped static volume array, forward projecting thecropped static volume array into a corresponding projection image togenerate an intermediate projection dataset, and subtracting theintermediate projection dataset from the projection image to generatethe refined dataset.
 20. The method of claim 15 wherein the secondfour-dimensional reconstruction is performed within one or moreboundaries of the refined dataset.